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The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented. [ABSTRACT FROM AUTHOR]

Today, the world is transitioning from traditional energy to clean, renewable sources. The petroleum sector is to play a role in this transition by supporting material and energy needs related to developing new energy systems. It is, therefore, vital that in upcoming years, the petroleum sector runs in a smart and efficient way, which can be achieved by coordination and the meaningful integration of decision-making issues in petroleum supply chains (PSCs). The existing literature on PSC optimization reveals a research gap; specifically, there is an insufficient level of technological detail considered while planning capacities of new infrastructures and its impact on the efficiency of further operations, specifically in the push segment of the PSC. This paper proposes a mixed-integer nonlinear programming model for planning capacities and coordinating activities within the mentioned PSC segment. The infrastructure capacity planning model covers technological details such as hydraulics and pump systems' operational efficiency. The results reveal that the proposed model and its technological decision-making criterion of minimizing energy consumption drive infrastructural choices and operational modes to achieve machinery performance close to the best efficiency point. Also, the computational results demonstrate how traditional (minimum-cost) approaches lead to inefficient energy use while producing and transporting hydrocarbons. The proposed framework aims to facilitate the preliminary design stage of projects undertaken by engineering contractors in the energy sector. [ABSTRACT FROM AUTHOR]

This paper presents research on an unconventional electric machine. It is a hybrid excited machine which includes the features of three types of machines: the Permanent Magnet Synchronous Machine, the Synchronous Machine, and the Synchronous Reluctance Machine. Therefore, a broad literature review related to the above-mentioned types of machines was constructed. The well-known Permanent Magnet assisted Synchronous Reluctance Machine joins features of Permanent Magnet Synchronous Machine and Synchronous Reluctance Machine topologies. This paper shows the results of the innovative design of the Hybrid Excited Permanent Magnet assisted Synchronous Reluctance Machine, which additionally has advantages of the Synchronous Machine. In the article the basic methods of electromagnetic flux control and the designs using them are also presented. Finally, the results of simulation studies of the effect of the stator skew on the machine performance are described. [ABSTRACT FROM AUTHOR]

Based on the magnetic gear effect, the field-modulated permanent-magnet machine (FMPMM) can realize the unequal pole design of the rotor PM field and the stator armature magnetic field. With the advantages of high torque density and high efficiency, the FMPMM has been widely studied in low-speed direct-drive applications. As a kind of machine excited by PMs, the performance of the FMPMM was affected by the demagnetization state. However, the method for establishing the FMPMM demagnetization model based on a finite element analysis (FEA) presented some problems, including tedious repeated modeling work and long calculation time-consuming under fine subdivision. Therefore, in this paper, a six-phase surface-mounted FMPMM was taken as an example, and an equivalent magnetic network (EMN) model was proposed for evaluating the machine performance under demagnetization. In order to realize the rapid establishing EMN models under diverse demagnetization types, the variable coercivity of PM was introduced. Furthermore, for the purpose of improving the calculation accuracy and shortening the calculation time, the least square method was used in fitting and analyzing the discrete results. Then, in order to verify the validity of the proposed EMN model, a prototype was fabricated and a testing platform was built. The air-gap flux density and the no-load back EMF obtained by the FEA, the proposed EMN model, and the experimental testing were compared. The results showed that the proposed EMN model can realize the rapid modeling and accurate analysis of the six-phase surface-mounted FMPMM under diverse demagnetization types. [ABSTRACT FROM AUTHOR]

The fault tolerance proprieties of multiphase induction machines make them candidate technologies for future electrified transport systems. Indeed, the use of multiphase drives is primarily recommended for their redundancy, which allows them to handle increasing power demands as well as provide fault-tolerant operation. One of the most common failures in polyphase induction machines is open-phase fault (OPF). Under this degraded mode, machine performance deteriorates with increased torque ripples and copper losses. This work investigates the fault-tolerant operation of dual star induction machines (DSIM) with two connected neutral points in which a new post-fault operation technique is introduced to manage the OPFs. The machine was modeled on the natural reference frame, and no transformations were used. The phase opening is caused by inserting a high-value resistor in series with the faulty phase, which cancels the current flowing in the latter. A post-fault reconfiguration strategy that reorganizes the power converter in order to cancel torque ripples by exploiting the principle of multi-biphase modeling of the DSIM is proposed. The performance of the proposed reconfiguration strategy was verified through detailed simulation results. [ABSTRACT FROM AUTHOR]

Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing. [ABSTRACT FROM AUTHOR]

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study's results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022. [ABSTRACT FROM AUTHOR]

Today, switched reluctance motors (SRMs) represent a promising technology for the long-term sustainability of electrified transportation, mainly due to their simpler structure, lower production cost, and robust configuration compared to other motor technologies. Notwithstanding, high acoustic noise and torque ripple are two performance imperfections that have prevented the widespread implementation of SRMs. This paper presents different structural design techniques to reduce the acoustic noise of an 8/6 SRM, while maintaining the electromagnetic performance of the machine. For each technique, a corresponding multiphysics FEA analysis of the motor's performance is presented. The accuracy of the multiphysics model is confirmed experimentally using acoustic noise measurements obtained from a four-phase 8/6 SRM. Then, several structural techniques have been investigated on the 8/6 SRM represented in two main categories: stator-housing modifications and rotor modifications. The best design strategies are then combined to improve the acoustic noise level of the 8/6 SRM while maintaining its performance. [ABSTRACT FROM AUTHOR]

Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships' environmental performance-related data, collected during ships' voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships' environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster. [ABSTRACT FROM AUTHOR]

Future European strategies to reduce dependence on foreign markets for energy supply and energy production will rely on the further exploitation of the primary sector. Lignocellulosic feedstock for bioenergy production is a valuable candidate, and dedicated crops such as giant reed (Arundo donax L.), miscanthus (Miscanthus × giganteus), reed canary grass (Phalaris arundinacea L.), and switchgrass (Panicum virgatum L.) have been proven to be suitable for extensive cultivation on marginal lands. The present review aimed at providing a comprehensive picture of the mechanical strategies available for harvesting giant reed, miscanthus, reed canary grass, and switchgrass that are suitable for the possible upscaling of their supply chain. Since harvesting is the most impactful phase of a lignocellulosic supply chain in dedicated crops, the associated performance and costs were taken into account in order to provide concrete observations and suggestions for future implementation. The findings of the present review highlighted that the investigated species have a sufficient technology readiness level concerning mechanical harvesting for the upscaling of their cultivation. All the species could indeed be harvested with existing machinery, mostly derived from the context of haymaking, without compromising the work productivity. [ABSTRACT FROM AUTHOR]

Consuming energy sources and greenhouse gas emission are one of the most prominent problems of the latest century. Most of the energy consumed globally and carbon dioxide emissions originate from electric motors used in the industry. Therefore, researchers have recently focused on the production of highly efficient, eco-friendly, and low-priced machines: Synchronous Reluctance Machines. In this study, the design and comprehensive Finite Element Analysis of a TLA-SynRM including the effects of the number of conductors per slot and wire diameter directly affecting stator slot fill factor and crucial for obtaining more realistic results from experiments has been initially carried out. Moreover, power factor, saliency ratio, and efficiency of the novel SynRM are enhanced by utilizing a fine-tuning process based on d- and q-axes flux paths. Additionally, the layer structure of the initial design is changed to a double-layer structure to improve the performance of the machine in the fine-tuning process. In the final step of this study, the machine has been manufactured, and experiments have been accomplished. This study has concluded that the novel SynRM have low torque ripple, high power factor, saliency ratio, and efficiency, whose value is within the range of IE5 efficiency class. [ABSTRACT FROM AUTHOR]

The interior permanent magnet synchronous machine (IPMSM) has been widely used in industrial applications due to its several favorable advantages. To further improve the machine performance, an improved nonlinear predictive controller for the IPMSM is proposed. In this paper, the maximum torque per ampere control law is firstly transformed to a linear function, according to the first−order Taylor expansion, and integrated with the control strategy. On this basis, an improved predictive control method is formulated by designing an optimized cost function through the input−output feedback linearization. Then the integral action is introduced to eliminate the influence of the load mutation and improve the steady−state control precision of the system. The stability of the control method is ensured by compelling the outputs to track the desired references without steady−state error. Finally, the simulation was established to verify the effective of the improved control method. Simulation results showed that the machine can reach the given reference speed without steady−state error within a short process, which means the machine has excellent dynamic and static performances. Furthermore, the machine has higher torque−to−current ratio by making full use of the reluctance torque. The simulation results verify the effectiveness of the improved control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates magnet demagnetization characteristics of the modular permanent magnet machine. The influence of flux gaps on magnet flux density, losses distribution, torque and demagnetization are analyzed for different operating conditions. The magnet demagnetizations caused by three sources, such as the PM field, the armature field, and the magnet temperature rise, are individually investigated using the frozen permeability method. Furthermore, coupled electromagnetic (EM)-thermal modelling is also adopted in this paper to fully reveal the advantages of the modular machine in improving machine EM performances. This is essential due to the temperature-dependent properties of the machines, such as the magnet remanence, coercivity, and copper resistivity. For comparison propose, the EM performances with a particular focus on the demagnetization withstand capability for both the modular and non-modular machines are investigated based on the EM-thermal coupling. It is found that, compared to the non-modular machine, the modular machine can achieve higher torque, higher efficiency, and better demagnetization withstand capability. [ABSTRACT FROM AUTHOR]

This paper presents a new topology of an axial flux-switching memory machine with series-type permanent magnets (SPMs) to improve the flux regulation capability. The key idea was to combine low coercive force (LCF) PMs, which can be magnetized and demagnetized readily, with high coercive force (HCF) PMs in series to provide a variable air-gap magnetic flux. The flux regulating principle and the forward magnetization effect of HCF PMs on LCF PMs are presented and discussed. Based on 3D finite element analysis (FEA), the magnetization variation of LCF PMs and the electromagnetic performances of the machine under different magnetization states were investigated. Simulation results showed that the air-gap magnetic flux could be weakened by up to 45% by using a magnetization current impulse, significantly extending the PM motor's flux-weakening speed range. Finally, a prototype machine was built, and experiments were performed to validate the predictions. The basic experimental validation shows that the measured back-EMF and the flux regulating characteristic matched the 3D FEA results well. [ABSTRACT FROM AUTHOR]

To address the increasing interest towards more environmentally friendly naval transportation and the introduction of IMO2020 restrictions on pollutant emissions onboard ships, the present work details the preliminary design of a mini gas turbine engine, i.e., a gas turbine engine with an output power up to 5 MW, for onboard energy generation. In comparison to conventional propulsion systems, gas turbine units benefit from known compactness, which can be further enhanced by employing single-stage uncooled radial machines, according to similar works in the field. As such, the present paper aims to set up a complete procedure that allows a reliable and fast (i.e., requiring a limited computational effort) preliminary design of one-stage centrifugal compressors and radial turbines operating at a high pressure ratio via the use of classical one-dimensional theory. The aerodynamic design outputs in terms of forces and torques are then used to perform a preliminary mechanical design of the shaft by means of a one-dimensional finite element model with commercial software to estimate the corresponding shaft line stress. Despite some necessary geometrical and modeling simplification of the design problem, which results in the unavailability of detailed information on individual components, the employed procedure nevertheless allows a comprehensive overview of the possibilities in terms of maximum machine performance achievable at an early design stage with the associated limited computational requirements. The design procedure and the geometry achieved for the application are presented along with aerodynamic and structural results. [ABSTRACT FROM AUTHOR]

The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed. [ABSTRACT FROM AUTHOR]

This paper suggests a method to improve the performances of the Dual Stator Axial Flux Spoke-type Permanent Magnet (DSAFSPM) machines with phase group concentrated coil (PGCC) windings, by incorporating continuous and discrete step-skewing along with a special winding connection. The purpose of the study is to mitigate the cogging torque and torque ripples while increasing the output torque so it ameliorates the machine performance at minimum cost for various applications such as wind power plants and electric vehicles (EVs). Cogging torque produces noise and vibrations which degrade the machine's performance and reduces its life span. The proposed winding sequence enhances the output torque by improving its distribution factor along with the use of continuous skew and step-skew magnets. This research work improved the cogging torque and torque ripples with the help of skew techniques while output torque is increased by the proposed winding sequence. Further harmonics and ripples are also mitigated by the proposed winding sequence. The overall machine volume is kept constant along with the magnet size and the design parameters for fair performance analysis. Comparative analysis of these machines is performed using three-dimensional (3-D) time-stepped finite element analysis (FEA). Proposed model I and proposed model II reduce the harmonics by 42% and 23%, respectively. By using continuous skew and discrete step-skew magnets, cogging torque is reduced up to 81.5% and 75%, respectively. This reduction in cogging torque reduces the noise and vibration in machines which assists the machines to perform a smooth operation. The reduction in output torque ripples in proposed model I is 60.8% while that of proposed model II is 59.3%. [ABSTRACT FROM AUTHOR]

Electrical utilities performance is measured by various indicators, of which the most important are very dependent on the interruption time after a failure in the network has occurred, such as SAIDI. Therefore, they are constantly looking for new techniques to decrease the fault location and repair times. A possibility to innovate in this field is to estimate the failed network component when a fault occurs. This paper presents the conclusion of an analysis carried out by the authors with the aim to estimate failure types of underground MV networks based on observable indirect variables. The variables needed to carry out the analysis must be available shortly after the failure occurrence, which is facilitated by a smart-grid infrastructure, to allow for a quick estimation. This paper uses the groundwork already carried out by the authors on ambient variables, historical variables, and disturbance recordings to design an estimator to predict between four MV cable network failure types. The paper presents relevant analyses on the design and performance of various machine learning classification algorithms for estimation of the types of MV cable network failures using real-world data. Optimization of performance was carried out, resulting in an estimator with an overall 68% accuracy rate. Accuracy rates of 94% for cable failure, 63% for excavations, and 79% secondary busbar failures were achieved; as for cable joints, the accuracy was poor due to the difficulty to identify a feature that can be used to separate this failure type from cable failures. Future work to improve that accuracy is discussed. [ABSTRACT FROM AUTHOR]

In high performance electric machines, the increase of fundamental frequency leads to additional losses in the winding due to parasitic effects such as the associated skin and proximity effects. In the first part, this paper presents an investigation into accurate modelling of AC losses in the winding using numerical methods and their experimental verification. Then, using experimental motorette setups, this research provides a comparative study between fixed strand positioning and fixed conductor shapes on the AC losses in the winding. It is shown that the exact position of strands in the conductor is not a critical factor; however, it is very important to control the conductor shape inside the slot. In the final section of this paper, an investigation into the relationship between AC losses in the winding and copper filling factor is presented. It is shown experimentally that counter-intuitive design choices such as using a lower copper fill factor and thicker strand diameters may be beneficial in achieving the highest overall efficiency. [ABSTRACT FROM AUTHOR]

Linear alternators work seamlessly with Free-Piston Stirling Engines (FPSE) in the energy conversion process. This research concentrates on the design and development of a tubular slot-spaced Permanent Magnet Linear Alternator (PMLA) to be coupled with the FPSE. In an attempt to increase the power density of the machine over conventional PMLAs, a slot space is added to reduce the total mass of the stator and a parametrical study is carried out for the same. Numerically analyzing the fundamental parameters such as change of stator and magnet materials, and operating conditions frequency and stroke length. The effects of slotting are studied to understand the skinning effects on the tooth and the performance variation of the machine. A modified model is obtained to surpass 100 W from the numerical analysis of the parametric variations. Putting the machine to extreme limitations, the study conducted upon variations of parameters obtained a stable maximum power density of 186 W/kg and produced a power of 921 W for the designed PMLA. The study outlines the variation seen in the performance of the machine in such diverse conditions they go through during their life cycle. [ABSTRACT FROM AUTHOR]

The bulldozer is a mobile earthmoving machine with a differentially steered mobile base and an onboard manipulator used for soil cutting and transportation. Grading the ground to match a desired contour is an end-effector path-following task, with required joint rates dependent on mobile base motion. The offline planning of travel velocity profiles that respect the available hydraulic flowrate limits is difficult due to uncertainties in the machine–soil interactions. Hence, we propose a flow-bounded velocity controller enabling accurate automatic grading with online velocity scaling. The capacity of hydrostatic transmission and manipulator actuators, as well as the desired velocity, are considered when deciding the commanded velocity reference for the mobile base. Our dynamic simulation results show that, with the proposed controller, a desired ground profile is cut accurately when the machine operates at its performance limits. Comparison to constant velocity driving shows that errors in blade positioning are reduced dramatically. Constant velocity selected to keep the flow within limits results in longer completion times compared to our solution, making it more time optimal. Furthermore, the rpm of the diesel engine can be reduced to save fuel without compromising control performance. [ABSTRACT FROM AUTHOR]

These surrogate models replace the machine model and estimate the output parameters of the machine based on the input parameters. Induction machines are one of the most important technical applications for both the industrial world and private use. The main purpose of this paper was to develop methods to reduce the computational effort in the design and optimization of IMs using the finite element method or analytical methods. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among different IM conditions, i.e., a machine in good physical condition, a machine with one broken rotor bar, a machine with two broken rotor bars, a machine with damage on the bearing outer race, and a machine with an unbalanced mechanical load. [Extracted from the article]

This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542. [ABSTRACT FROM AUTHOR]

To overcome energy crises, attention is being paid to saving energy from household appliances. Traditional compressors are in need of being replaced by efficient linear compressors. This paper presents a new tubular moving magnet linear oscillating actuator (TMM-LOA) for compressor application. The proposed topology utilizes outer mover topology with separators between the mover and stator modules. The number of stator and mover modules can be increased or decreased based on the requirement. The addition of a separator avoids the flux cancellation and makes the proposed topology fault-tolerant. The design variables are optimized by using a parametric sweep, and the performance in terms of thrust force is observed. Both the static and transient analyses were performed to analyze the machine performance at various currents and stroke. Both mechanical and electrical resonance phenomena are discussed. The efficiency of the proposed TMM-LOA is calculated for one, two and three modules. Finally, the proposed topology is compared with other topologies proposed in the literature to show the superiority of the proposed design. [ABSTRACT FROM AUTHOR]

Nowadays, induction machines (IMs) are widely used in industrial and transportation applications (electric or hybrid ground vehicle or aerospace actuators) thanks to their significant advantages in comparison to other technologies. Indeed, there is a large demand for IMs because of their reliability, robustness, and cost-effectiveness. The objective of this paper is to improve the reliability and performance of the three-phase induction machine in case of mechanical sensor failure. Moreover, this paper will discuss the development and proposal of a fault-tolerant controller (FTC), based on the combination of a vector controller, two virtual sensors (an extended Kalman filter, or EKF, and a sliding mode observer, or SMO) and a neural voting algorithm. In this approach, the vector controller is based on a new structure of a back-stepping sliding mode controller, which incorporates a double integral sliding surface to improve the performance of the induction machine in faulty operation mode. More specifically, this controller improves the machine performance in terms of having a fast response, fewer steady-state errors, and a robust performance in the existence of uncertainty. In addition, two voting algorithms are suggested in this approach. The first is based on neural networks, which are insensitive to parameter variations and do not need to set a threshold. The second one is based on fuzzy logic. Finally, validation is carried out by simulations in healthy and faulty operation modes to prove the feasibility of the proposed FTC. [ABSTRACT FROM AUTHOR]

In this study, a torque enhancement principle for a stator permanent-magnet (PM) Vernier machine with a consequent pole structure is proposed. The novelty of the manuscript is the explanation of how stator PM Vernier machines benefit from consequent pole structure. Although a similar concept of stator PM Vernier machines with a consequent pole structure has been investigated in previous studies, a clear torque enhancement principle was absent. The keys to torque enhancement are the decrement of nonworking harmonics and increment of working harmonics. To demonstrate these phenomena, the torque contributions from the harmonics were calculated. Subsequently, the performances of the conventional machine and consequent pole machine were compared to verify the enhancement principle. The electromagnetic performances, including the back electromotive force and torque, were compared. The results show that the stator PM Vernier machine benefits significantly from torque enhancement using the consequent pole structure, and the torque enhancement principle was verified. [ABSTRACT FROM AUTHOR]

Rolling-element bearings play vital roles in the dynamic vibration performance of the whole machinery. Hence, accurate modeling and assessment of the rolling-element bearing are beneficial for the well understanding of the vibration response of rolling-element bearing. However, cage slip is usually ignored in the traditional rolling-element bearing modeling methods, which has a direct influence on the rotating speed and friction force of the rolling elements. To settle the modeling problem of rolling-element bearing with cage slip, in this study, a nonlinear dynamic model with multiple degrees of freedom of the roller bearing is established. The cage slip, the motion of each roller, nonlinear contact, damping, and friction are taken into consideration. With the proposed method, a nonlinear traction model is presented to describe the friction forces induced by cage slip. Furthermore, both the friction force acting on rolling elements and the effects of cage slip on the vibration response are investigated based on the established model. Some comparisons between the proposed modeling method with cage slip and the classical method without cage slip are made. The results show that the friction force applied to the balls increases with the cage slip, friction coefficient, rotational speed, and radial load. A slight cage slip could be beneficial for reducing the vibration energy of rolling-element bearing; however, it will result in more friction loss and impact components. [ABSTRACT FROM AUTHOR]

In this paper, a Rankine cycle test system is established to recover exhaust energy from a 2.0 L gasoline engine. Experiments on the system's performance are carried out under various working conditions. The experimental results indicate that the recovery power of the expander is strongly related to the load and speed of the gasoline engine. It is found that when the output power of the gasoline engine is 39.8-76.6 kW, the net power of the expander is 1.8-2.97 kW, which is equivalent to 3.9%-4.9% of the engine power. The performance simulation shows that the mass flow rate, power output, and isentropic efficiency of the piston expander are directly determined by the intake valve timing. Selecting a suitable intake valve timing can optimize the performance of the expander. The simulation results show that a 1 kW increment in power can be obtained only by selecting an optimum intake open timing. The experimental results further verify that the single-valve piston expander, because of its small dimensions, simple structure, and high speed, is appropriate, and has great potential for energy recovery of gasoline engine exhaust and has good prospects for engineering applications. [ABSTRACT FROM AUTHOR]

This study focused on the design optimization of permanent magnet synchronous linear motors (PMSLM) that are applied in microsecond laser cutting machines. A new design optimization method was introduced to enhance PMSLM performances in terms of motor thrust, thrust ripple, and inductive electromotive force (EMF). Based on accurate 3D finite element analysis (3D-FEA), a multiple support vector machine (multi-SVM) was proposed to build a non-parametric quick calculation model by mapping the relation between multivariate structure parameters and multivariate operation performances. The gravity center neighborhood algorithm (GCNA) was also applied to search the global optimal combination of the structure parameters by locating the gravity center of the multi-SVM model. The superiority and validity of this method are verified by experiments. [ABSTRACT FROM AUTHOR]

This paper reviews the serviceability of hydrous ethanol as a clean, cheap and green renewable substitute fuel for spark ignition engines and discusses the comparative chemical and physical properties of hydrous ethanol and gasoline fuels. The significant differences in the properties of hydrous ethanol and gasoline fuels are sufficient to create a significant change during the combustion phase of engine operation and consequently affect the performance of spark-ignition (SI) engines. The stability of ethanol-gasoline-water blends is also discussed. Furthermore, the effects of hydrous ethanol, and its blends with gasoline fuel on SI engine combustion characteristics, cycle-to-cycle variations, engine performance parameters, and emission characteristics have been highlighted. Higher water solubility in ethanol-gasoline blends may be obviously useful and suitable; nevertheless, the continuous ability of water to remain soluble in the blend is significantly affected by temperature. Nearly all published engine experimental results showed a significant improvement in combustion characteristics and enhanced engine performance for the use of hydrous ethanol as fuel. Moreover, carbon monoxide and oxides of nitrogen emissions were also significantly decreased. It is also worth pointing out that unburned hydrocarbon and carbon dioxide emissions were also reduced for the use of hydrous ethanol. However, unregulated emissions such as acetaldehyde and formaldehyde were significantly increased. [ABSTRACT FROM AUTHOR]

In this study, the authors experimentally investigate the performance of the organic Rankine cycle (ORC) and screw expander under the influence of supply pressure and pressure ratio over the expander. Three tests were performed with expander pressure ratios of 2.4–3.5, 3.0–4.6, and 3.3–6.1, which cover the over-expansion and under-expansion operating modes. The test results show a maximum expander isentropic efficiency of 72.4% and a relative cycle efficiency of 10.5% at an evaporation temperature of 101 °C and condensation temperature of 45 °C. At a given pressure ratio over the expander, a higher supply pressure to the expander causes a higher expander isentropic efficiency and higher cycle efficiency in the over-expansion mode. However, in the under-expansion mode, the higher supply pressure results in a lower expander isentropic efficiency and adversely affects the cycle efficiency. The results also show that under the condition of operation at a given pressure ratio, a higher supply pressure yields a larger power output owing to the increased mass flow rate at the higher supply pressure. The study results demonstrate that a screw-expander ORC can be operated with a wide range of heat sources and heat sinks with satisfactory cycle efficiency. [ABSTRACT FROM AUTHOR]

A rapid compression machine (RCM) test bench is developed in this study. The performance characterization and auto-ignition performance tests are conducted at an initial temperature of 293 K, a compression ratio of 9.5 to 16.5, a compressed temperature of 650 K to 850 K, a driving gas pressure range of 0.25 MPa to 0.7 MPa, an initial pressure of 0.04 MPa to 0.09 MPa, and a nitrogen dilution ratio of 35% to 65%. A new type of hydraulic piston is used to address the problem in which the hydraulic buffer adversely affects the rapid compression process. Auto-ignition performance tests of the RCM are then performed using a DME–O2–N2 mixture. The two-stage ignition delay and negative temperature coefficient (NTC) behavior of the mixture are observed. The effects of driving gas pressure, compression ratio, initial pressure, and nitrogen dilution ratio on the two-stage ignition delay are investigated. Results show that both the first-stage and overall ignition delays tend to increase with increasing driving gas pressure. The driving gas pressure within a certain range does not significantly influence the compressed pressure. With increasing compression ratio, the first-stage ignition delay is shortened, whereas the second-stage ignition delay is extended. With increasing initial pressure, both the first-stage and second-stage ignition delays are shortened. The second-stage ignition delay is shortened to a greater extent than that of the first-stage. With increasing nitrogen dilution ratio, the first-stage ignition delay is shortened, whereas the second-stage is extended. Thus, overall ignition delay presents different trends under various compression ratios and compressed pressure conditions. [ABSTRACT FROM AUTHOR]

The noise generated by different types of fans used in the turbomachinery industry is a topic that has been studied for many years. However, researchers are still looking for a universal solution to reduce noise while maintaining the performance of these machines. This paper, as a contribution to the research, presents the results of numerical investigations of an axial fan installed in a pipeline with a circular cross-section. In particular, the focus was on investigating the sensitivity of the sound power level to changes in selected design and operational parameters of this fan. The simulation studies used the unsteady Reynolds-averaged Navier–Stokes (URANS) approach and the Ffowcs Williams–Hawkings (FW-H) analogy implemented in Ansys Fluent. [ABSTRACT FROM AUTHOR]

Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance. [ABSTRACT FROM AUTHOR]

Condition monitoring, diagnostics, and prognostics are key factors in today's competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment. [ABSTRACT FROM AUTHOR]

Oriented steel has higher permeability and lower losses in the direction of orientation (the rolling direction) than non-oriented steel. However, in the transverse direction, oriented steel typically has lower permeability and higher losses. The strategic use of oriented steel in a modular Permanent Magnet Synchronous Machine (PMSM) stator can improve machine performance, particularly when compared to a machine designed with non-oriented steel, by increasing both torque and efficiency. Typically, steel manufacturers provide magnetic properties only in the rolling and transverse directions. Furthermore, in modern Finite Element Analysis (FEA) software, the magnetic properties between the rolling and transverse directions are interpolated using an intrinsic mathematical model. However, this interpolation method has proven to be inaccurate; to resolve this issue, an improved model was proposed in the literature. This model requires the magnetic properties of the oriented steel in between the rolling and transverse directions. Therefore, a procedure for extracting the magnetic properties of oriented steel is required. The objective of this work is to propose a method of determining the magnetic properties of oriented steel beyond just the oriented and transverse directions. In this method, flux-injecting probes, also known as sensors, are used to inject and control the flux density in an oriented steel segmented stator in order to extract the properties of the oriented steel. These extracted properties are then used to model an oriented steel modular stator PMSM. The machine's average torque and core losses are compared with conventional, non-modular, non-oriented steel stator PMSM, and modular, non-oriented steel stator PMSM. It is shown that both the average torque and the core loss of the oriented steel modular stator PMSM have better performance at the selected number of segments than either of the two non-oriented steel stators. [ABSTRACT FROM AUTHOR]

A new Single-sided Variable Flux Permanent Magnet Linear Machine with flux bridge in mover core is proposed in this paper. The flux bridge prevents the leakage flux from the mover and converts it into flux linkage, which greatly influences the performance of the machine. First, a lumped parameter model is used to find the suitable coil combination and no-load flux linkage of the proposed machine, which greatly reduces the computational time and drive storage. Secondly, the proposed machine replaces the expensive rare earth permanent magnets with ferrite magnets and provides improved flux controlling capability under variable excitation currents. Multivariable geometric optimization is utilized to optimize the leading design parameters like split ratio, stator pole width, width and height of permanent magnet, flux bridge width, the width of mover's tooth, and stator slot depth at constant electric and magnetic loading. The optimized design increases the flux linkage by 44.11%, average thrust force by 35%, thrust force density by 35.02%, minimizes ripples in thrust force by 23%, and detent force by 87.5%. Furthermore, the results obtained by 2D analysis are verified by 3D analysis. Thermal analysis is done to set the operating limit of the proposed machine. [ABSTRACT FROM AUTHOR]

Mining machinery and equipment used in modern mining are equipped with sensors and measurement systems at the stage of their production. Measuring devices are most often components of a control system or a machine performance monitoring system. In the case of headers, the primary task of these systems is to ensure safe operation and to monitor its correctness. It is customary to collect information in very large databases and analyze it when a failure occurs. Data mining methods allow for analysis to be made during the operation of machinery and mining equipment, thanks to which it is possible to determine not only their technical condition but also the causes of any changes that have occurred. The purpose of this work is to present a method for discovering missing information based on other available parameters, which facilitates the subsequent analysis of machine performance. The primary data used in this paper are the currents flowing through the windings of four header motors. In the method, the original reconstruction of the data layout was performed using the R language function, and then the analysis of the operating states of the header was performed based on these data. Based on the rules used and determined in the analysis, the percentage structure of machine operation states was obtained, which allows for additional reporting and verification of parts of the process. [ABSTRACT FROM AUTHOR]

This paper describes a study on the improvement of the dehydration performance of the conventional washing machine model. Recently, as interest in improving the dehydration performance of washing machines has increased, the need for a study on a high-speed electric motor has emerged. However, this conventional spoke-type PMSM has difficulty in speeding up due to the following problems. First, field weakening control is indispensable for high-speed operation of an electric motor. This control method is a big problem in causing torque drop and irreversible demagnetization of the motor. Moreover, the centrifugal force increases during high-speed operation, which adversely affects the stiffness of the motor. Therefore, in this paper, a new rotor shape of spoke-type PMSM was proposed to solve the above problem. [ABSTRACT FROM AUTHOR]

Multi-phase motors and generators are regarded with great fault tolerance capability, especially on open-circuit faults. Various mathematics analytical methods are applied for their fault control. In this paper, a fault-tolerant control strategy with asymmetric phase current for the open-circuit faults with arbitrary phases in the six-phase PMSM (six-phase permanent magnetic synchronous motor, 6P-PMSM) system, is proposed for better electrical and dynamical performance of the machine. An innovative mathematical model for PMSM under one to four-phase-open circuit faults are established considering the asymmetry of the machine. Combining with time-varying relations in machines' working conditions, targeted decoupling transformation matrixes of every kind of open-circuit faults are settled by voltage equations under different faults. Modified control strategy with a connection between the neutral point and the inverter's DC side is presented, which aims at increasing the system redundancy and reducing the amplitude of phase currents. Besides, improved control loops with two layers are put forward as well, with which the PMSM system acquires fewer harmonics in phase current and smoother electromagnetic torque. Simulation and experimental results of open-circuit faults are provided for verification of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, the energy diagnostic of tractor performance consists in evaluating the energy (fuel consumption per hectare—dm3 ha−1) for a given agricultural operation and in combining it with working capacity, also called productivity (area productivity—ha h−1). One of the methods of solving this problem is the identification of the functioning process of the machine unit. A model of the process of the machine unit performance was developed, considering the operation of the rear linkage system of the implement with the force control adjustment system. In order to analyze the system, a mathematical model of the system function was built: tractor-implement-soil, defining the physical connections and interdependencies between the individual subsystems of the system. Based on this model, a simulation model was developed and implemented in the Matlab/Simulink environment. The Simulink package was used to test the performance of the machine set. The efficiency indicators according to the adopted criteria were calculated in the evaluation block. To evaluate the process, the technical and operational parameters of the tractor, the type and parameters of the tool, and soil properties were taken into account. The results of simulation studies obtained on a validated model are consistent with experimental data from appropriate soil conditions. [ABSTRACT FROM AUTHOR]

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%. [ABSTRACT FROM AUTHOR]

Permanent magnet Vernier machine (PMVM) is a strong candidate for direct-drive applications in low-speed region because its power characteristic is higher than conventional permanent magnet machine (PMM). In this paper, the design of a dual-stator PMVM (DSPMVM) with spoke-type rotor is introduced. As a radial motor with dual-stator configuration, one on the outer and inner side, the rotor is equipped with support bars for practical and simple manufacturing, which is the aim of this study. The characteristics and performance of the proposed machine with and without the support bar are examined with finite element analysis (FEA). The DSPMVM and the support were manufactured and tested through experiments to verify proposed structure. Both simulation and experiment results show that there is little to no difference in performance when the support bar is equipped. Furthermore, the average deviation between simulation and experiment results is approximately 7% which is within the acceptable range. [ABSTRACT FROM AUTHOR]

The modular multilevel converter is capable to reach high-voltage levels with high flexibility, high reliability, and high power quality as it became the standard solution for high-power high-voltage applications that operate with fixed frequency. However, in machine-drive applications, the modular multilevel converter shows critical problems since an extremely high submodule-capacitor voltage ripple occurs in the machine start-up and at low-speed operation, which can damage the converter. Recently, a new converter solution named modular multilevel series converter was proposed as a promising alternative for high-power machine-drive applications since it presented many important structural and operational advantages in relation to the modular multilevel converter such as the reduced number of submodule capacitors and the low submodule-capacitor voltage ripple at low frequencies. Even though the modular multilevel series converter presented a reduced number of capacitors, the size of these capacitors was not analyzed. This paper presents a detailed comparison analysis of the performance of the modular multilevel converter and the modular multilevel series converter at variable-frequency operation, which is based on the proposed analytical description of the submodule-capacitor voltage ripple in such topologies. This analysis concludes that the new modular multilevel series converter can be designed with smaller capacitors in comparison to the modular multilevel converter if these converters are used to drive electrical machines that operate within a range of low-frequency values. In other words, the modular multilevel series converter experiences extremely low submodule-capacitor voltage ripple at very low frequencies, which means that this converter solution presents high performance in the electrical machine start-up and at low-speed operation. [ABSTRACT FROM AUTHOR]

The paper critically reviews the current state of the art in flow inside sidewall gaps of hydraulic pumps and turbines. It describes the consequences of the presence of this type of flow in turbomachinery and then relates it to other physical phenomena that determine the behavior, operating characteristics, and overall performance of the machine. Despite the small dimensions of the rotor-stator spaces, the flow in these regions can significantly affect the overall flow field and, consequently, efficiency. The circulation of the fluid inside the gaps and secondary flow that is caused by rotating elements influences the disk friction losses, which is of great importance, especially in the case of low specific speed pumps and turbines. The flow pattern affects the pressure distribution inside a machine and, thus, generates axial thrust. The presence of secondary flow also significantly changes the rotordynamics and can bring about undesirable vibrations and acoustics issues. This article aims to review and summarize the studies that were conducted on the mentioned phenomena. Experimental and numerical studies are both taken into consideration. It proposes some requirements for prospective research in order to fill current gaps in the literature and reveals the upcoming challenges in the design of hydraulic machines. [ABSTRACT FROM AUTHOR]

The use of Pumps As Turbines (PATs) can be the best solution for exploiting small hydraulic resources. Pump manufacturers do not provide the performances of their machines running in reverse mode, thus several authors developed appropriate prediction models. Some of them can only correlate the pump Best Efficiency Point (BEP) to the PAT corresponding one; other ones are able to obtain the characteristic curves. In this paper, a review of these methodologies is presented with the aim to find the best strategy that allows a designer of a small hydropower plant to select the PAT to be used and to predict its characteristic curves. The study also highlights the possibility of disassembling some models in order to merge the best parts in a more reliable strategy. [ABSTRACT FROM AUTHOR]

Currently, there is increasing research interest in harnessing wind energy for power generation by means of non-conventional electrical machines e.g., flux-reversal machines. The flux reversal machine is usually designed using scarce rare–earth permanent magnet material which may be unattractive in terms of machine cost. In this study, an attempt is made to re-design the flux reversal machine with non-rare-earth ferrite permanent magnet for wind energy applications. Because these machines possess high cogging torque, which results in vibration and noise, that are detrimental to the machine performance, especially at low speeds, a novel combined skewed and circumferential rotor pole pairing method is developed. The proposed cogging torque reduction method is implemented in 2-dimensional finite element analysis modeling and comparatively analyzed with other existing stand-alone methods viz., skewing, and rotor pole pairing. The results show that the proposed method led to 94.8% and 71% reduction in the cogging torque and torque ripple compared to the reference generator, respectively. However, the calculated torque density is reduced by 13%. Overall, the electromagnetic performance of the proposed ferrite PM machine exhibits desirable qualities as an alternative design for the direct drive wind generator. [ABSTRACT FROM AUTHOR]

Unstable flow structures cause inevitable energy losses in all power energy systems, including turbomachines. In this study, a set of analyses was conducted with the use of spectral maps on the pressure signals obtained from an industrial centrifugal compressor. The spectral maps provide one a detailed visualization of the flow conditions present in the machine along the performance curve and to distinguish the flow phenomena present prior to the surge. The method accuracy is especially useful in detecting the inlet recirculation. The study was conducted at four impeller rotational speeds with varying loads imposed by a valve at the outlet. At each speed, the machine experienced different stages of unstable flow conditions prior to the surge. Five main frequency peaks that appeared in all cases were identified and discussed. The surge was observed at all impeller speeds. At lower ones, however, it appeared at higher valve closures. At higher speeds, the surge was much more intense. The study has also shown that the inlet recirculation appears also for the closed-type industrial impeller. The phenomenon was present in all conditions. The higher impeller speed, the faster onset of the inlet recirculation was. This structure has a strong potential for an early instability warning because it appears in various types of impellers, has a very particular spectral structure and its positioning is very predictable. This study gives another example of the inlet recirculation universality and potential for efficient anti-surge protection. [ABSTRACT FROM AUTHOR]

Sugar beet is an extensive crop of great agronomic value with significant productive and economic returns and Ukraine's sugar beet accounts for about 5.1% of the overall world production. Sugar beets and the by-products resulting from its manufacturing transformation are a significant renewable energy resource. A new high-quality performance prototype of a sugar beet top harvester, front mounted on a tractor, was built by the authors in Ukraine. The aim of this study is to evaluate the main performance parameters related to the operation of this new machine. Field tests were carried out linking the prototype to a wheel tractor, whilst suitable sensors measured the significant kinematic and dynamic parameters, allowing experimental data collection to assess the machine's performance parameters. The entire technological process of harvesting and transporting the beet tops to the beet top storage unit required power ranging from 6.42 to 17.65 kW. At the topmost tested forward speed, the required tractor traction force was less than 1.9 kN with the power required by the shaft that drives the screw conveyor ranging from 3.1 to 4.6 kW. This value was the lowest for a speed of the tractor–beet top harvesting machine aggregate ranging from 0.9 to 1.2 m · s − 1 . [ABSTRACT FROM AUTHOR]

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve-based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation, despite their need for accurate location information and bias correction, as well as their insufficient replication of extreme events and short-term power ramps. In this paper, we assessed how time series generated by machine learning models (MLMs) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we applied neural networks to one wind speed input dataset derived from MERRA2 reanalysis with no location information and two with additional location information. The resulting time series and RN time series were compared with actual generation. All MLM time series feature an equal or even better time series quality than RN, depending on the characteristics considered. We conclude that MLM models show a similar performance to RN, even when information on turbine locations and turbine types is unavailable. [ABSTRACT FROM AUTHOR]